8 ways to improve your data visualizations

Organizations worldwide, revolve around data like planets revolve around the sun. Since data is so central to organizations, there are certain data visualization tools that help them understand data to make better business decisions. A lot more data is getting churned out and collected by organizations than ever before. So, how to make sense of all this data?

Humans are visual creatures and our human brain processes visual information far better than textual information. In fact, presentations that use visual aids such as colors, shapes, images, etc, are found to be far more persuasive according to a research done by University of Minnesota back in 1986.

Data visualization is one such process that easily translates the collected information into engaging visuals. It’s easy, cheap and doesn’t require any designing expertise to create data visuals. However, some professionals feel that data visualization is just limited to slapping on charts and graphs when that’s not actually the case. Data visualization is about conveying the right information, in a way that enhances the audience’s experience.

So, if you want your graphs and charts to be more succinct and understandable, here are eight ways to improve your data visualization process:

1. Get rid of unneeded information

Less is more in some cases and the same goes for data visualization.

Using excessive color, jargons, pie charts and metrics take away focus from the important information. For instance, when using colors, don’t make your charts and graphs a rainbow instead use a specific set of colors with a clear purpose and meaning. Do you see the difference color and chart make to visualization in the below images?

Similarly, when it comes to expressing your data, note how people interact at your workplace. Keep the tone of your visuals as natural as possible to make it easy for the audience to interpret your data. For metrics, only show the ones that truly bring value to your storytelling. Filter out the ones that are not so important to create less fuss. Tread cautiously while using pie charts as they can be difficult to understand sometimes and also, get rid of the elements on a chart that cause unnecessary confusion.

2. Use conditional formatting for tabular data

Data visualization doesn’t need to use fancy tools or designs. Take your standard excel table for example. Do you want to point out patterns or outliers in your data? Conditional formatting is a great tool for people working with data. It involves making simple rules on a given data and once that’s done, it’ll highlight only the data that matters the most to you. This helps quickly track the main information.

Conditional formatting can be used for different things. It can help spot duplicate data in your table. You need to set bounds for the data using the built-in conditional formatting. It’ll then format the cells based on those bounds, highlighting the data you want.

For instance, if sales quota of over 65% is good, between 65% and 55% is average, and below 55% is poor, then with conditional formatting, you can quickly find out who is meeting the expected sales quota, and who is not.

3. Add trendlines to unearth patterns for prediction

Another feature that can amp up your data visualization is trendlines. They observe the relationship between two variables from your existing data. They are also are useful for predicting future values. Trendlines are simple to add and help discover trends in the given data set.

It also show data trends or moving averages in your charts. Depending on the kind of data you’re working with, there are a number of trendlines out there that you can use on your visualizations. Questions like whether a new strategy seems to be working in favor of the organization can be answered with the help of trendlines. This insight, in turn, helps predict new outcomes for the future. Statistical models are used in trendlines to make predictions.
Once you add trend lines to a view, it’s up to you to decide how you want them to look and behave.

4. Implement filter by rule to get more specific

Filter helps display just the information that you need. Using filter by rule, you can add filter option to your dataset. Organizations produce huge amounts of data on a regular basis. Suppose you want to know which employees within your organization are consistent performers. So, instead of creating a visualization that includes all the employees and their performances, you can filter it down, so that it shows only the employees who are always doing well.

Similarly, if you want to find out which day the sales went up or down, you can filter it to show results for only the past week or month depending upon your preference.

5. For complex or dense data representation, add hierarchy

Hierarchies eliminate the need to create extra visualizations. You can view data from a high level and dig deeper into the specifics of the data as you come up with questions based on the data. Adding a hierarchy to the data helps club multiple information in one visualization.

For instance, if you create a hierarchy that shows the total sales achieved by different sales representative within an organization in the past month. Now, you can further break this down by selecting a particular sales rep, and then you can go even further by selecting a specific product assigned to that sales rep. This cuts down on a lot of extra work.

6. Make visuals more appealing by formatting data

Data formatting takes only a few seconds but it can make a huge difference when it comes to the audience interpreting your data.

It makes the numbers appear more visually appealing and easier to read for the audience. It can be used for charts such as bar charts and column charts. Formatting data to show a certain number of decimals, comma separators, number font, currency or percentage can make your visualization process more engaging.

7. Include comparison for more insight

Comparisons provide readers a better perspective on data. It can both improve and add insights to your visualizations by including comparisons to your charts.

For instance, in case you want to inform your audience about organization’s growth in current as well as the past year then you can include comparison within the visualization. You can also use a comparison chart to compare between two data points such as budget vs actually spent.

8. Sort data to improve readability

Again, sorting through data is a great way to make things easy for the audience when dealing with huge quantities of data. For instance, if you want to include information about the highest and lowest performing products, you can sort your data. Sorting can be done in the following ways:

Ascending – This helps sort the data from lowest to highest.

Descending – This sorts data from highest to lowest.

Data source order – Sorts the data in the order it is sorted in the data source.

Alphabetic – Data is alphabetically sorted.

Manual – Data can be sorted manually in the order you prefer.

Effective data visualization helps people interpret the information in data that could not be seen before, to change their minds and prompt action. These were some of the tricks and features to take your data visualization game to the next level.

There are different data visualization tools available in the market to choose from. Tableau and Microsoft Power BI are among the top ones that offer great features for data visualization.

So, now that we’ve got you covered with some of the best practices for data visualization, it’s your turn to put these tips to practice and create some strong visual data stories. Do you have any DataViz tips to share with our readers? Please add them in the comments below.